An inexact successive quadratic approximation method for L-1 regularized optimization
Küçük Resim Yok
Tarih
2016
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We study a Newton-like method for the minimization of an objective function that is the sum of a smooth function and an regularization term. This method, which is sometimes referred to in the literature as a proximal Newton method, computes a step by minimizing a piecewise quadratic model of the objective function . In order to make this approach efficient in practice, it is imperative to perform this inner minimization inexactly. In this paper, we give inexactness conditions that guarantee global convergence and that can be used to control the local rate of convergence of the iteration. Our inexactness conditions are based on a semi-smooth function that represents a (continuous) measure of the optimality conditions of the problem, and that embodies the soft-thresholding iteration. We give careful consideration to the algorithm employed for the inner minimization, and report numerical results on two test sets originating in machine learning.
Açıklama
Anahtar Kelimeler
Sparse Optimization, Inexact Proximal Newton, Orthant-Based Quasi-Newton, Newton, Selection
Kaynak
Mathematical Programming
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
157
Sayı
2
